Assessing impact of agroecological interventions in Niger through remotely sensed changes in vegetation

Water scarcity is a major challenge in the Sahel region of West Africa. Water scarcity in combination with prevalent soil degradation has severely reduced the land productivity in the region. The decrease in resiliency of food security systems of marginalized population has huge societal implications which often leads to mass migrations and conflicts. The U.S. Agency for International Development (USAID) and development organizations have made major investments in the Sahel to improve resilience through land rehabilitation activities in recent years. To help restore degraded lands at the farm level, the World Food Programme (WFP) with assistance from USAID’s Bureau for Humanitarian Assistance supported the construction of water and soil retention structures called half-moons. The vegetation growing in the half-moons is vitally important to increase agricultural productivity and feed animals, a critical element of sustainable food security in the region. This paper investigates the effectiveness of interventions at 18 WFP sites in southern Niger using vegetative greenness observations from the Landsat 7 satellite. The pre - and post-intervention analysis shows that vegetation greenness after the half-moon intervention was nearly 50% higher than in the pre-intervention years. The vegetation in the intervened area was more than 25% greener than the nearby control area. Together, the results indicate that the half-moons are effective adaptations to the traditional land management systems to increase agricultural production in arid ecosystems, which is evident through improved vegetation conditions in southern Niger. The analysis shows that the improvement brought by the interventions continue to provide the benefits. Continued application of these adaptation techniques on a larger scale will increase agricultural production and build resilience to drought for subsistence farmers in West Africa. Quantifiable increase in efficacy of local-scale land and water management techniques, and the resulting jump in large-scale investments to scale similar efforts will help farmers enhance their resiliency in a sustainable manner will lead to a reduction in food security shortages.


Results
Pre-and post-intervention analysis. Figure  The figure clearly shows that ares surrounding the intervention sites also showed a modest increase in NDVI values (5%), the intervened sites showed significantly higher NDVI values post-intervention (>25%) in 2019 when compared to 2013 period.
The peak NDVI value during the peak growing season (Aug-Oct) for each of the sites showed a significant improvement post-intervention as seen by Landsat 7 (Fig. 2). On average, the peak NDVI value increased by 49.7% (from 0.217 to 0.325) across all sites after the interventions (Table 1). NDVI increased at four sites by more than 60%, with the maximum enhancement of 81.1% observed for Kafat (NDVI increased from 0.185 to 0.335). We observed the smallest NDVI increase of 29.7% (from 0.202 to 0.262) for Boussarague; however, the difference in NDVI is also positive and statistically significant using two-tailed t-test (p<0.001).
The rainfall during months preceding the peak growing season is expected to have a significant impact on vegetation conditions. Therefore, in this analysis, we considered the total rainfall data for two months prior to the peak growing season. Table 1 shows the annual peak NDVI values (Aug-Oct) before and after the interventions for each of the sites, in addition to the average total rainfall from two months before the peak NDVI season June to August. The table shows that the total rainfall post-intervention is higher (12.3% on average) for all sites, which can be attributed to the above-average precipitation in recent years (2018)(2019)(2020). Although rainfall is one of the primary drivers of vegetative growth, the table clearly indicates that the change in peak NDVI values is not linearly related to the differences in total rainfall before and after the intervention. Even though the rainfall increased by 12%, the NDVI jumped by nearly 50% at these sites after the intervention, as compared to the vegetation before the intervention. Figure 3(a) shows the scatter plot between the peak NDVI and total rainfall before and after the intervention. The results show that there was some, although weak, relationship between the NDVI and rainfall before www.nature.com/scientificreports/ the intervention ( R 2 = 0.24). Interestingly, the relationship between the two variables was reduced by 36% ( R 2 = 0.15) after the intervention. This confirms the assumption that there are factors other than total rainfall that contributed to the vegetative growth in the region and that these external factors seem to play an even greater role post-intervention period, with the construction of half-moons being the obvious change between the preand post-intervention NDVI estimates. Despite above-normal rainfall during the later stages of the study years (2018-2020, Fig. 9a), the scatter plot (Fig. 3b) between the percent difference in total rainfall and NDVI peak shows that there is no linear relationship between these two variables. The analysis indicates that the impact of increased rainfall in increasing vegetative growth is negligible after the interventions. Examining the monthly NDVI values give insights into the natural vegetative growth cycle before and after the intervention. For instance, monthly NDVI values across Kafat and Danja are shown in Fig. 4. The shaded portion represents the standard deviation of mean NDVI values across multiple polygons at the same site (4    Fig. 4) are substantially higher than pre-intervention (red lines in Fig. 4) from August through December. The increase is consistent across all polygons as evident by the standard deviations. The Danja site has only one polygon ( ∼ 42 ha) that was developed in 2015. Danja allows us to analyze and compare a few years of data both before and after interventions. Moreover, the post-intervention period contains at least three years of below-normal rainfall, with 2017 as one of the most significant rainfall deficit years (deficit of more than 100 mm from the long-term average), thus providing a good mix of rainfall distributions to assess the impact of interventions using NDVI. Similarly, the mean monthly NDVI values were close (although post-intervention values were consistently slightly higher) in the relatively dry months. The difference became even more pronounced during the wet months, with the mean peak rising from 0.27 to 0.33. All intervention sites showed similar trends where NDVI values after intervention were consistently higher than the NDVI values before intervention. This higher difference in NDVI value is also observed for a couple of months after the rainy season, indicating prolonged greenness. Overall, for dry months the NDVI differences Table 1. Summary of mean annual peak NDVI values (Aug-Oct) before and after intervention with the mean total rainfall (Jun-Aug) from 2010 to 2020.  Figure 3. Scatter plots (a) showing relationship between the mean peak NDVI and total rainfall before (red) and after the intervention (blue) and (b) scatter plot between percent differences in NDVI and rainfall before and after intervention for each of the sites. www.nature.com/scientificreports/ before and after interventions were in the range of 0.015-0.03, which more than doubled to 0.044-0.063 during the months when NDVI values peaked. When analyzed together, Table 1 and Figure 4 show that the half-moons at all sites were associated with a statistically significant 49.7% increase in the peak vegetation as compared to the pre-intervention period, even after accounting for differences in rainfall during the time periods.
Control analysis. During the planning and implementation of these interventions, no control site was specifically identified. Therefore, for this analysis, we have taken an indirect approach where a pair of polygons from the same site that were developed a few years apart were used as an experiment (or intervened) and control sites. This approach ensured that the later-intervened area was suitable for the intervention, and hence is an appropriate comparator. A total of 7 pairs (Table 2) across multiple sites were found that were used for control analysis. Figure 5 shows the NDVI time series for Dargue and Karkara at the intervention and control sites. The baseline NDVI values for both intervention and control sites were similar or in some cases less than the control sites. During the experiment period, as hypothesized, the NDVI values in the intervention site significantly increased compared to the control sites. After the end of the experiment period (when both sites were developed), the NDVI values of the control sites began to match the NDVI values from the intervention site due to the increased greenness. The analysis shows that interventions have a clear positive impact on vegetative greenness in the region. The analysis shows that the NDVI values during the baseline period for both experimental and control sites were similar. The p-value was found to be 0.4, therefore, the null hypothesis (that the means are statistically similar) cannot be rejected. However, during the experiment period, the average difference in mean NDVI between the control and experimental site was statistically significant (0.028) with a p<0.05. The increase in vegetation brought by the interventions sustained till date, which illustrates the sustainability of the interventions to provide the positive results for years to come. That is particularly notable, given the increasing threat of erratic rainfall brought by climate change.
Overall, the NDVI analysis shows that the vegetation during the baseline period was comparable between the two sets of polygons. During the experiment period, the vegetation increased more than 25% in the polygons with half-moon interventions compared to control polygons (Fig. 6).  www.nature.com/scientificreports/  www.nature.com/scientificreports/ BACI analysis. We performed the Before-After Control-Impact (BACI) 50,51 analysis to further quantify the impact of interventions during the experiment period. 51 suggested a random sampling of control and experiment sites over space and time. However, due to limited number of sample pairs, we used a randomized bootstrap (with 50 iterations) to select a combinations of the control and experiment site with varied area and intervention periods as proxy of true random sampling model design. The analysis shows that there was a BACI contrast of −0.061 with nearly 56% of relative contrast in NDVI value. The negative value of BACI contrast (in the units of NDVI) indicates that greenness has increased in the experimental site with respect to the control site, relative to NDVI values during the baseline period. The relative contrast (a ratio of BACI contrast to mean baseline NDVI from the experiment site) is a unitless normalized value used to express the impact of intervention as a percentage.

Discussion
The satellite-derived vegetative greenness can be used to assess the impact of intervention activities. However, certain limitations must be considered when interpreting the results. In this preliminary study, we assume that precipitation, and thus the available moisture content in the soil, play the most prominent role in natural vegetative growth (most of the restoration sites being pastoral) and can explain some of the variances in annual vegetative growth conditions. It must be noted that soil moisture (or precipitation) alone may not be the only influencing factor. Further, the satellite data used in this analysis are based on visible and near-infrared bands that cannot penetrate through clouds. Therefore, under cloudy conditions, there could be significant data gaps. We mitigated this challenge by using Landsat data from 2010 only, ensuring data availability of more than 74% for months when NDVI values peaked. In this analysis, we analyzed relatively larger-sized polygons, however, Landsat's spatial resolution of 30 m may not be appropriate for smaller (<1 ha, approx.) intervention sites. The use of harmonized multi-sensor data products including more recent Landsat 8 and 9 data along with Sentinel-2 data has the potential to mitigate some of these limitations 52,53 . The inclusion of data from cloud-penetrating synthetic aperture radar (SAR) onboard Sentinel-1 can further minimize data gaps. Another gap in this analysis is testing the robustness of the interventions under drought conditions. For most of the sites, the rainfall during the post-intervention was higher than the long-term mean. Therefore, we could not assess the efficacy of the interventions under drought conditions. Continuous monitoring of these sites for longer periods of time will give better insight into the effectiveness of the interventions in a sustainable manner.
The results from this case study clearly shows that satellite observations can be used for impact analysis while addressing some of the challenges laid out by 8,11 and others. Although, this study focused on the vegetative conditions as a proxy of intervention impact assessment, other satellite derived environmental variables such as the evapotranspiration 54-56 , soil moisture 57,58 etc. can also provide critical information related to the interventions, however such observations are derived using either thermal or microwave bands and therefore are of relatively coarser resolution than visible band driven NDVI. Several attempts have been made to downscale the coarser scale soil moisture 59,60 and evapotranspiration 61,62 to field scales that can be utilized for such applications.
This analysis covers 18 sites, and multiple polygons at each site, in southern Niger where WFP assisted to develop half-moons between 2013 and 2020 using a pre/post assessment approach. The satellite-based NDVI measurements were used to assess the impact of the half-moons on vegetative conditions. Using Landsat 7 imagery, our analysis showed a statistically significant increase in the peak NDVI values of nearly 50% after the half-moons were constructed compared to pre-intervention years, an indication of improved grazing land for pastoralists and cropland for farmers. Analysis of vegetation at a smaller set of 7 intervention sites and nearby control sites suggests that the interventions had a significant impact on NDVI values, whereas the control sites showed modest improvement in vegetation conditions. An increase in vegetation greenness of more than 25% was found at the intervention sites, as compared to the control sites. Overall, the analysis shows that the halfmoons contribute to a substantial improvement in the greenness of landscapes. Additional work is needed to link the increased greenness to crop productivity analysis; however, these results provide actionable evidence to support scaling up half-moon interventions as an effective land management practice to increase agricultural production in arid ecosystems and build resilience to drought for subsistence farmers.

Methods
Study area. WFP is working on more than 300 sites for SWC interventions in southern Niger and has recorded specific geographic outlines of half-moons at 18 sites (Fig. 7) from four regions -Maradi, Zinder, Tahoua, and Tillaberi. Each site has several areas in which half-moons are concentrated (termed as intervention polygons hereon) that were either developed as pastoral or agricultural half-moons (Fig. 7). Some of the sites had only one polygon (e.g. Koona, Raffa and Danja) whereas Karkara had 14 polygons (Fig. 8c). However, it should be noted that not all of the polygons from a given site were intervened simultaneously. Most polygons were intervened during 2015 (24) and 2018 (26), while relatively few were intervened in 2016 (4) and 2017 (6) (Fig. 8a). These 18 sites include 101 intervention polygons and cover approximately 4400 ha of the total area developed with an average of nearly 42 ha per site. Only 14 sites had an area greater than 100 ha while 62 sites covered less than 25 ha of area. Figure 8b shows the area distribution for all the sites.
Most of the fields in southern Niger are rainfed making farmers vulnerable to climate conditions and variable rainfall patterns. On average, in the last 40 years (1982-2021) southwestern Niger has experienced nearly 450 mm of rainfall annually. However, total rainfall can vary significantly annually (standard deviation of 70 mm, approx.) with as low as 270 mm (1984) to more than 630 mm (2020) in a single year. Figure 9a shows standardized anomalies of annual rainfall in southern Niger. In general, rainfall has been either normal (less than ± 0.5 deviations from normal) or above average. In particular, the years 2018-020 showed significantly higher than normal rainfall in the region. Furthermore, the region has a very distinct rainy season (Jun-Sept) when more  www.nature.com/scientificreports/ than 90% of the total annual rainfall is observed (Fig. 9b). Therefore, the SWC measures in the region can provide much-needed moisture availability for longer duration.
Satellite data. With a repeat cycle of 16 days and moderately high spatial resolution (30 m), the historical record of Landsat data extends to the 1980s. Several vegetation indexes have been developed to quantify vegetation health, including the NDVI, Enhanced Vegetation Index (EVI 63 ), and Soil Adjusted Vegetation Index (SAVI 64 ) etc. Vegetation changes are slow (spanning over several days) and Landsat's 16-day revisit frequency can capture the natural vegetative cycle. However, frequent cloud cover poses a challenge to the use of visible and near-infrared band-based indexes such as NDVI. That is particularly true for data collected before 2010 when relatively fewer satellite overpasses are available for analysis. In this study, we used Landsat 7 data from Google Earth Engine image collection after accounting for quality flags, clouds, and cloud shadows 65 . Moreover, the rainfall data for contextual assessment was available from Climate Hazards Infrared Precipitation with Station Data (CHIRPS 66 ). CHIRPS is a satellite-driven rainfall product that has been corrected using ground observations from across the globe. A 5 day (pentad) CHIRPS product (1982-2021) at 5 km spatial resolution was used in this study. Gridded CHRIPS precipitation data has been extensively evaluated and applied across the globe 66-69 . Temporal analysis. Although, the study period includes the year 2020, any site that were developed in the year 2020 were not included in this analysis due to limited post-intervention sample size. Therefore, out of 108 possible polygons from 18 sites, only 101 were examined in this study. The average size of the polygons is approximately 42 ha, equivalent to about 450 Landsat pixels. Cloud and cloud shadow masked Landsat imageries were used to compute NDVI for each pixel within each polygon and then aggregated over the polygon for temporal analysis. In this study, the NDVI values were analyzed at monthly and annual scales. The annual peak (95 th ) percentile data were analyzed to assess the variability in polygons' average NDVI values over the years. Since this study was designed retrospectively, we could not randomly assign matching control sites. It is possible that despite being from close proximity and similar agro-ecological classifications, the physical characteristics could vary, thereby influencing the results. To address this challenge, we leveraged the longitudinal characteristics of the Landsat record to establish a retrospective "baseline" using the preceding years of the sites that were developed at a later stage (after 2017) as controls for the treatment sites that were developed between 2013-2015 from the same region.  www.nature.com/scientificreports/ Control analysis. During the planning and implementation of these interventions, no control site was specifically identified. Therefore, for this analysis, we have taken an indirect approach where a pair of polygons from the same site that were developed a few years apart were used as an experiment (or intervened) and control sites. This approach ensured that the later-intervened area was suitable for the intervention, and hence is an appropriate comparator. We applied three criteria for selecting the control and intervention pairs by ensuring: (a) that the polygons were nearby (as part of the same larger site) (b) that the sites are from the same livelihood zones (agricultural or pastoral) (c) that the polygons have interventions at least 3 years apart.
The first criterion ensures that both sites have similar weather patterns and cropping/grazing practices. The second ensures the similarity in vegetation types and patterns, and the final criterion ensures that the evaluation can focus on those years where the intervention sites are expected to increase in vegetation, whereas the control sites are anticipated to follow the pre-intervention vegetation patterns. Effectively, the site that was developed earlier becomes the intervention site or experimental site, whereas a site with a later intervention date can be used as control site until an intervention happens at that polygon. Using this approach, a total of 7 pairs (Table 2) across multiple sites were found that can be used for control analysis. The duration of control years ranged from 3 to 6 depending upon the site and intervention years. The minimum area selected in either control or intervention was 13 ha, (150 Landsat pixels) to ensure a statistically substantial pixel count for estimating NDVI. We divided NDVI data into two time periods:

Data Availability
Model codes and sample data are publicly available at https:// github. com/ Vikal p86/ Resil ience_ Mappi ng. Further data/analysis used in this study also available from the corresponding author on reasonable request. www.nature.com/scientificreports/